Critical data analysis: Accuracy and privacy in forensic genetics -and- Representation in science

Rori Rohlfs
Date and time: 
Thu, Mar 3 2022 - 12:00pm
Rori Rohlfs
San Francisco State University
  • Thanh Nguyen

Forensic genetic identification has a huge social impact in the United States, with over 20 million individuals profiled in the federal database, and DNA evidence increasingly common in court. Despite this prominence, we still lack 1) the quantified accuracy of emerging forensic genetic technologies, particularly in light of population genetic variation, and 2) clarity on what (if any) protected medical information could be inferred through forensic genetic profiles. In this talk, I will discuss analyses regarding both questions based on publicly available population genetic data. We show that the identification accuracy varies in relation to human population genetic diversity. Further, we show that accuracy deteriorates with inappropriate population genetic assumptions. When considering the medical privacy and forensic profiles, we identified five loci used in forensic identification with significant associations to the expression levels of neighboring genes. We further identify plausible molecular mechanisms of association with medically relevant genes, raising policy questions regarding the seizure, storage, and uses of forensic genetic profiles.

This applied population genetics research builds on the rich theoretical literature provided by our scientific forebearers. As a group, the scientists often credited for the foundation of the field underrepresent persons excluded because of race or ethnicity (PEERs) and persons excluded because of gender (PEGS). We question if this underrepresentation is exacerbated by biased practices in assigning academic credit. I will discuss our analysis of gender in the practices of authorship and acknowledgements in the historical theoretical population genetics literature.


Rori Rohlfs is an Assistant Professor of Biology at San Francisco State University. She earned dual Bachelor’s degrees in Computer Science and Biology at Carnegie Mellon University, followed by a PhD in Genome Sciences, specializing in statistical genetics, from the University of Washington. With the support of an NSF Fellowship, she performed postdoctoral research in molecular evolution at the University of California, Berkeley. Her lab studies how genetic variation and subsequent molecular mechanisms contribute to diversity within and between species, as well as the social impacts of technologies based on genetic variation. This manifests as three federally funded lines of research: 1) investigating the reliability and impact of forensic genetic technologies; 2) applying statistical models to study the evolution of genome regulation; and 3) clarifying how systems of oppression influence science, and attempting to improve social justice within science.